As of June 2024, the most popular database management system (DBMS) worldwide was Oracle, with a ranking score of 1244.08; MySQL and Microsoft SQL server rounded out the top three. Although the database management industry contains some of the largest companies in the tech industry, such as Microsoft, Oracle and IBM, a number of free and open-source DBMSs such as PostgreSQL and MariaDB remain competitive. Database Management Systems As the name implies, DBMSs provide a platform through which developers can organize, update, and control large databases. Given the business world’s growing focus on big data and data analytics, knowledge of SQL programming languages has become an important asset for software developers around the world, and database management skills are seen as highly desirable. In addition to providing developers with the tools needed to operate databases, DBMS are also integral to the way that consumers access information through applications, which further illustrates the importance of the software.

As of June 2024, the most popular relational database management system (RDBMS) worldwide was Oracle, with a ranking score of 1244.08. Oracle was also the most popular DBMS overall. MySQL and Microsoft SQL server rounded out the top three.

MariaDB Galera Cluster is the most commonly used database for OpenStack components worldwide, according to the OpenStack User Survey in 2023. As of that time, 48 percent of respondents reported the use of MariaDB Galera Cluster for the OpenStack components in their organizations

In 2023, over 45 percent of surveyed software developers worldwide reported using PostgreSQL, the highest share of any database technology. Other popular database tools among developers included MySQL and SQLite.

The global in-memory database market size was valued at USD 10.5643 billion in 2025 and is projected to grow at a compound annual growth rate (CAGR) of 16.19% during the forecast period (2025-2033). The growth of the market is attributed to the increasing adoption of in-memory databases in various industries to improve data processing speed and performance. In-memory databases store data in the computer's main memory (RAM) instead of on a physical disk, which allows for faster data access and retrieval. Key market drivers include the growing volume of data, the need for real-time data analysis, and the increasing adoption of cloud computing. The growing volume of data, often referred to as "big data," is a significant factor driving market growth. The need for real-time data analysis is another key driver, as in-memory databases can provide faster data access than traditional databases. The increasing adoption of cloud computing is also driving market growth, as cloud-based in-memory databases offer scalability and flexibility. Recent developments include: March 2023: SAP revealed SAP Datasphere, the company's next-gen data management system. It gives customers easy access to business-ready data across the data landscape. SAP also announced strategic agreements with top data and AI companies, including Collibra NV, Confluent Inc., Databricks Inc., and DataRobot Inc., to improve SAP Datasphere and allow organizations to build a unified data architecture that securely combines SAP software data and non-SAP data., June 2023: IBM has released a new tool to aid corporations in monitoring their carbon footprint pollution across cloud services and improve their sustainability as they move to hybrid and multi-cloud environments. The IBM Cloud Carbon Calculator, an AI-powered dashboard, is now available to everyone. It can help clients access emissions data for various IBM Cloud tasks, such as AI, high-performance computing (HPC), and financial services., SingleStoreDB for December 2022 was announced last year by IBM and SingleStore. With IBM introducing SingleStoreDB as a solution, businesses are now moving forward in their strategic relationship to deliver the quickest, most scalable data platform that supports data-intensive programs. For Azure, AWS, and Microsoft Azure marketplace, IBM has released SingleStoreDB as a service., In April 2022, McObject issued the eXtremeDB/rt database management system (DBMS) for Green Hills Software’s Integrity RTOS. The first-ever commercial off-the-shelf (COTS) real-time DBMS satisfying basic criteria of temporal and deterministic consistency in data is known as eXtremeDB/rt. It was initially conceived and built as an integrated in-memory database system for embedded systems., November 2022: Redis, provider of real-time in-memory databases, and Amazon Web Services have formed a multi-year strategic alliance. It is a networked open-source NoSQL system that stores data on disk for durability before moving it to DRAM as required. As such, it can be used as a message broker cache, streaming engine, or database., December 2022: The largest Indian stock exchange, National Stock Exchange, opted for Raima Database Manager (RDM) Workgroup 12.0 In-Memory System as its foundational component for upcoming versions of its trading platform front-end called National Exchange for Automated Trading (NEAT)., On January 13th, 2021, Oracle launched Oracle Database 21c – the latest version of the world’s leading converged database available on Oracle Cloud with the Always Free tier of Oracle Autonomous Database included. It includes more than two hundred new features, according to Oracle’s press release, including immutable blockchain tables; In-Database JavaScript; native JSON binary data type; AutoML for in-database machine learning (ML); persistent memory store; enhancements, including improvements regarding graph processing performance that support sharding, multitenant, and security., Stanford engineers have developed a new chip to increase the efficiency of AI computing in August 2022. Stanford engineers have created a more efficient and flexible AI chip that could bring the power of AI into tiny edge devices., In-Memory Database Market Segmentation,

Relational

NoSQL

NewSQL

,

Online Analytical Processing (OLAP)

Online Transaction Processing (OLTP)

,

Transaction

Reporting

Analytics

,

North America

US

Canada

Europe

Germany

France

UK

Italy

Spain

Rest of Europe

Asia-Pacific

China

Japan

India

Australia

South Korea

Australia

Rest of Asia-Pacific

Rest of the World

Middle East

Africa

Latin America

, . Potential restraints include: Security And Data Privacy Concerns 26.

The global database server market is anticipated to grow at a CAGR of XX% during the forecast period of 2023 to 2029, expanding from a market size of USD XXX million in 2023 to USD XXX million by 2029. This growth can be attributed to the growing demand for data storage and processing, the increasing adoption of cloud computing, and the emergence of new database technologies, such as NoSQL and in-memory databases. The growing need for data storage and processing is one of the primary drivers of growth for the database server market. The increasing volume of data being generated by businesses and organizations is putting a strain on traditional database systems, which are struggling to keep up with the demand. As a result, businesses are turning to database servers to help them store and manage their data more efficiently. The increasing adoption of cloud computing is also driving the growth of the database server market. The cloud offers several advantages over on-premises deployments, such as scalability, flexibility, and cost-effectiveness. As a result, more and more businesses are moving their databases to the cloud. The emergence of new database technologies, such as NoSQL and in-memory databases, is also driving the growth of the database server market. These new technologies offer several advantages over traditional database systems, such as scalability, performance, and flexibility. As a result, they are becoming increasingly popular among businesses.

The most common location for developed applications, databases, and services in 2023 was in a cloud service, as reported by 48 percent of all survey respondents worldwide. Next in line was locally with a share of 45 percent.

BackgroundIn the search for better anticancer drugs, computer-aided drug design (CADD) techniques play an indispensable role in facilitating the lengthy and costly drug discovery process especially when natural products are involved. Anthraquinone is one of the most widely-recognized natural products with anticancer properties. This review aimed to systematically assess and synthesize evidence on the utilization of CADD techniques centered on the anthraquinone scaffold for cancer treatment.MethodsThe conduct and reporting of this review were done in accordance to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) 2020 guideline. The protocol was registered in the “International prospective register of systematic reviews” database (PROSPERO: CRD42023432904) and also published recently. The search strategy was designed based on the combination of concept 1 “CADD or virtual screening”, concept 2 “anthraquinone” and concept 3 “cancer”. The search was executed in PubMed, Scopus, Web of Science and MedRxiv on 30 June 2023.ResultsDatabases searching retrieved a total of 317 records. After deduplication and applying the eligibility criteria, the final review ended up with 32 articles in which 3 articles were found by citation searching. The CADD methods used in the studies were either structure-based alone (69%) or combined with ligand-based methods via parallel (9%) or sequential (22%) approaches. Molecular docking was performed in all studies, with Glide and AutoDock being the most popular commercial and public software used respectively. Protein data bank was used in most studies to retrieve the crystal structure of the targets of interest while the main ligand databases were PubChem and Zinc. The utilization of in-silico techniques has enabled a deeper dive into the structural, biological and pharmacological properties of anthraquinone derivatives, revealing their remarkable anticancer properties in an all-rounded fashion.ConclusionBy harnessing the power of computational tools and leveraging the natural diversity of anthraquinone compounds, researchers can expedite the development of better drugs to address the unmet medical needs in cancer treatment by improving the treatment outcome for cancer patients.

AimIn this review, we investigated how Machine Learning (ML) was utilized to predict all-cause somatic hospital admissions and readmissions in adults.MethodsWe searched eight databases (PubMed, Embase, Web of Science, CINAHL, ProQuest, OpenGrey, WorldCat, and MedNar) from their inception date to October 2023, and included records that predicted all-cause somatic hospital admissions and readmissions of adults using ML methodology. We used the CHARMS checklist for data extraction, PROBAST for bias and applicability assessment, and TRIPOD for reporting quality.ResultsWe screened 7,543 studies of which 163 full-text records were read and 116 met the review inclusion criteria. Among these, 45 predicted admission, 70 predicted readmission, and one study predicted both. There was a substantial variety in the types of datasets, algorithms, features, data preprocessing steps, evaluation, and validation methods. The most used types of features were demographics, diagnoses, vital signs, and laboratory tests. Area Under the ROC curve (AUC) was the most used evaluation metric. Models trained using boosting tree-based algorithms often performed better compared to others. ML algorithms commonly outperformed traditional regression techniques. Sixteen studies used Natural language processing (NLP) of clinical notes for prediction, all studies yielded good results. The overall adherence to reporting quality was poor in the review studies. Only five percent of models were implemented in clinical practice. The most frequently inadequately addressed methodological aspects were: providing model interpretations on the individual patient level, full code availability, performing external validation, calibrating models, and handling class imbalance.ConclusionThis review has identified considerable concerns regarding methodological issues and reporting quality in studies investigating ML to predict hospitalizations. To ensure the acceptability of these models in clinical settings, it is crucial to improve the quality of future studies.

BackgroundWe sought to conduct this comprehensive systematic review and meta-analysis to assess the prevalence of depression, anxiety, and sleep disturbance in Iranian medical students and resident physicians.MethodsA systematic search was conducted on 23 December 2023 in PubMed/MEDLINE, Web of Science, Scopus, and Iranian national databases. We pooled the prevalence of individual studies using the random effect model.ResultsOur systematic search showed 36 articles that meet the eligibility criteria. Most included studies were cross-sectional. The most used questionnaire to assess depression, anxiety, and sleep disturbance were Beck Depression Inventory (BDI), The Depression, Anxiety and Stress Scale—21 Items (DASS-21), and The Pittsburgh Sleep Quality Index (PSQI), respectively. The overall prevalence of depression, anxiety, and sleep disturbance among Iranian medical students were 43% (95%CI: 33%–53%%, I2 = 98%), 44% (95%CI: 31%–58%%, I2 = 99%), 48% (95%CI: 39%–56%%, I2 = 97%), respectively. The results of subgroup and meta-regression analyses showed questionnaires used and the place of the medical school were significantly associated with the prevalence of aforementioned outcomes. Funnel plot and Begg’s regression test did not show a significant source of funnel plot asymmetry for depression, anxiety, and sleep disturbance.ConclusionIn conclusion, our study showed that nearly half of the medical students had some type of depression, anxiety, and sleep disturbance problems. To address this serious national public health issue, efficient preventive measures, routine screenings, and prompt interventions are required.

The dynamics of coronavirus disease-19 (COVID-19) have been extensively researched in many settings around the world, but little is known about these patterns in Africa. 7540 complete nucleotide genomes from 51 African nations were obtained and analysed from the National Center for Biotechnology Information (NCBI) and Global Initiative on Sharing Influenza Data (GISAID) databases to examine genetic diversity and spread dynamics of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) lineages circulating in Africa. Utilising a variety of clade and lineage nomenclature schemes, we looked at their diversity, and used maximum parsimony inference methods to recreate their evolutionary divergence and history. According to this study, only 465 of the 2610 Pango lineages found to have existed in the world circulated in Africa after three years of the COVID-19 pandemic outbreak, with five different lineages dominating at various points during the outbreak. We identified South Africa, Kenya, and Nigeria as key sources of viral transmissions between Sub-Saharan African nations. These findings provide insight into the viral strains that are circulating in Africa and their evolutionary patterns. Methods Dataset mining and workflow SARS-CoV-2 genome sequences collected from Africa were obtained from NCBI database and GISAID database on February 26, 2023. 24415 African sequences were retrieved from both databases so as to examine the number of lineages circulating within Africa. The two databases had only 8044 complete genome sequences combined from Africa, and these sequences excluding those with low coverage using NextClade were retrieved to determine spread dynamics. 5908 sequences from 23 African countries were available in the NCBI and 2137 sequences from 41 African countries from GISAID database. The sequences were aligned using the online version of the MAFFT multiple sequence alignment tool, with the Wuhan-Hu-1 (MN 908947.3) as the reference sequence, and sequences with more than 5.0% ambiguous letters were removed. Duplicates were removed using goalign dedup software and only high quality African complete sequences remained (n=7540). Phylogenetic reconstruction Using IQ-TREE multicore software version v1.6.12 and NextClade, phylogeny reconstruction on the dataset was performed numerous times. Lineage classification PANGOLin, a web application was used to classify sequences into their lineages. The objective was to determine the SARS-CoV-2 lineages that are circulating in Africa that are most important from an epidemiological perspective, as well as the lineage dynamics within and across the African continent, due to the fact that this naming system integrates genetic and geographic data concerning SARS-CoV-2 dynamics. Phylogeographic reconstruction VOC, (VOI) and VUM were designated based on the WHO framework as of 20 January 2022. We included one lineage, namely A.23.1 and labelled it as VOI for the purposes of this analysis. This lineage was included because it demonstrated the continued evolution of African lineages into potentially more transmissible variants. VOI, VOC, and VUM that emerged on the African continent were marked. These were A.23.1 (VOI), B.1.351 and B.1.1.529 (VOC), B.1.640, and B.1.525 (VUM). Genome sequences of these five lineages were extracted from NCBI database for phylogeographic reconstruction. A similar approach to that described above (including alignment using online MAFFT) was employed. Phylogeographic reconstruction for all variants circulating in Africa and all VOI, VOC, and VUM was conducted using PASTML.

Oracle’s cloud services and license support division is the company’s most profitable business segment, bringing in over 39 billion U.S. dollars in its 2024 fiscal year. In that year, Oracle brought in annual revenue of close to 52 billion U.S. dollars, its highest revenue figure to date. Oracle Corporation Oracle was founded by Larry Ellison in 1977 as a tech company primarily focused on relational databases. Today, Oracle ranks among the largest companies in the world in terms of market value and serves as the world’s most popular database management system provider. Oracle’s success is not only reflected in its booming sales figures, but also in its growing number of employees: between fiscal year 2008 and 2021, Oracle’s total employee number has grown substantially, increasing from around 84,000 to 132,000. Database market The global database market reached a size of 65 billion U.S. dollars in 2020. Database Management Systems (DBMSs) provide a platform through which developers can organize, update, and control large databases, with products like Oracle, MySQL, and Microsoft SQL Server being the most widely used in the market.

[Note: Integrated as part of FoodData Central, April 2019.] The USDA National Nutrient Database for Standard Reference (SR) is the major source of food composition data in the United States and provides the foundation for most food composition databases in the public and private sectors. This is the last release of the database in its current format. SR-Legacy will continue its preeminent role as a stand-alone food composition resource and will be available in the new modernized system currently under development. SR-Legacy contains data on 7,793 food items and up to 150 food components that were reported in SR28 (2015), with selected corrections and updates. This release supersedes all previous releases. Resources in this dataset:Resource Title: USDA National Nutrient Database for Standard Reference, Legacy Release. File Name: SR-Leg_DB.zipResource Description: Locally stored copy - The USDA National Nutrient Database for Standard Reference as a relational database using AcessResource Title: USDA National Nutrient Database for Standard Reference, Legacy Release. File Name: SR-Leg_ASC.zipResource Description: ASCII files containing the data of the USDA National Nutrient Database for Standard Reference, Legacy Release.Resource Title: USDA National Nutrient Database for Standard Reference, Legacy Release. File Name: SR-Leg_ASC.zipResource Description: Locally stored copy - ASCII files containing the data of the USDA National Nutrient Database for Standard Reference, Legacy Release.

Authors, through Twitter API, collected this database over eight months. These data are tweets of over 50 experts regarding market analysis of 40 cryptocurrencies. These experts are known as influencers on social networks such as Twitter. The theory of Behavioral economics shows that the opinions of people, especially experts, can impact the stock market trend (here, cryptocurrencies). Existing databases often cover tweets related to one or more cryptocurrencies. Also, in these databases, no attention is paid to the user's expertise, and most of the data is extracted using hashtags. Failure to pay attention to the user's expertise causes the irrelevant volume to increase and the neutral polarity to increase considerably. This database has a main table named "Tweets1" with 11 columns and 40 tables to separate comments related to each cryptocurrency. The columns of the main table and the cryptocurrency tables are explained in the attached document. Researchers can use this dataset in various machine learning tasks, such as sentiment analysis and deep transfer learning with sentiment analysis. Also, this data can be used to check the impact of influencers' opinions on the cryptocurrency market trend. The use of this database is allowed by mentioning the source. Also, in this version, we have added the excel version of the database and Python code to extract the names of influencers and tweets. in Version(3): In the new version, three datasets related to historical prices and sentiments related to Bitcoin, Ethereum, and Binance have been added as Excel files from January 1, 2023, to June 12, 2023. Also, two datasets of 52 influential tweets in cryptocurrencies have been published, along with the score and polarity of sentiments regarding more than 300 cryptocurrencies from February 2021 to June 2023. Also, two Python codes related to the sentiment analysis algorithm of tweets with Python have been published. This algorithm combines RoBERTa pre-trained deep neural network and BiGRU deep neural network with an attention layer (see code Preprocessing_and_sentiment_analysis with python).

In 2023, traditional relational databases were the most popular tools used to build data lakes worldwide. With a total share of 22 percent of responses, it beat out the second place Delta Lake by 10 percent of survey votes.

Treatment of human excreta in humanitarian camps remains rare, leading to environmental and public health risks. Fecal sludge treatment (FST) can help reduce these risks. Our objective was to summarize barriers (hindering implementation) and facilitators (enabling implementation) to FST in humanitarian camps to inform guidance. We completed a systematic review of eight databases and 39 websites in 2020, with an update in April 2023. Documents were included if they assessed FST implementation, in a humanitarian camp setting, with primary data collection of at least output level indicators. Overall, 53 documents, including 75 FST interventions from 12 countries were included. We identified 424 barriers and 435 facilitators in 11 categories: performance (239), operation (146), technical (109), economic (78), environmental (59), spatial (55), social/cultural (47), temporal (44), safety (34), supply (29), and institutional (19). The most common facilitators of FST implementation were: high reduction efficiencies; rapid implementation with available technologies; low capital and operational costs; ease of operation and maintenance; and, achieving effluent discharge standards, effluent reuse, and safe discharge. The most common barriers included under- or over-designed systems with inappropriate materials, needing strong operational supervision and additional treatment, with effluents not meeting discharge standards. Future guidance should focus on recommendations to enable facilitators and hinder barriers. Limitations included that most of the research was from one country (Bangladesh), and in stable contexts. Strengths of this work include a holistic, broad, example-based summary of actual FST implementations in camps in humanitarian settings. This review can be used to develop guidance and checklists for implementing FST in humanitarian camps, and future research needed.

BackgroundIt is widely recognised that noncommunicable diseases are on the rise worldwide, partly due to insufficient levels of physical activity (PA). It is a particularly concerning health issue among children and adolescents in Arabic countries where cultural and environmental factors may limit their opportunity for engaging in physical activities.AimThis review sought to assess the effectiveness of school-based PA interventions for increasing PA among schoolchildren aged six to 18 years in Middle Eastern and Arabic-speaking countries.MethodsA systematic literature search was developed to identify studies reporting the evaluation of school-based PA interventions in Arabic-speaking countries. Four different databases were searched from January 2000 to January 2023: PubMed/MEDLINE, Web of Science, Scopus and CINAHL. Article titles and abstracts were screened for relevance. Full article scrutiny of retrieved shortlisted articles was undertaken. After citation searches and reference checking of included papers, full data extraction, quality assessment and narrative synthesis was undertaken for all articles that met the inclusion criteria. This review adhered to PRISMA guidelines for conducting systematic reviews.ResultsSeventeen articles met the inclusion criteria. Eleven articles reported statistically significant improvements in the levels of PA among their participants. Based largely on self-reported outcomes, increases in PA between 58% and 72% were reported. The studies with a follow-up period greater than three months reported sustained PA levels. There are a limited range of types of programmes evaluated and evaluations were only identified from 30% of the countries in the region. Relatively few studies focused solely on PA interventions and most of the interventions were multi-component (lifestyle, diet, education).ConclusionsThis review adds to the existing body of research about the efficacy of school-based interventions to increase physical activity levels. To date, few evaluations assess PA specific interventions and most of the interventions were multi-component including education components on lifestyle and diet. Long-term school-based interventions combined with rigorous theoretical and methodological frameworks are necessary to develop, implement and evaluate PA interventions for children and adolescents in Arabic-speaking countries. Also, future work in this area must also consider the complex systems and agents by which physical activity is influenced.

Medical start-ups (MedTech) significantly contribute to the development and commercialization of innovative healthcare solutions, driving advancements in technology, enhancing treatment effectiveness, and supporting public health. This study explores the main themes and concepts related to MedTech start-ups, examines the research methods used, and identifies major gaps in the literature. A scoping literature review was performed by searching the Scopus, PubMed, and Web of Science databases for publications from 2012 to 2023, focusing on MedTech start-ups in titles, abstracts, and keywords. References were analyzed using the Bibliometrix package in R, and a coupling network analysis was conducted, visualizing results on a Coupling Map to identify key research themes and gaps. The research identified 480 unique articles on MedTech start-ups. After removing duplicates and following a PRISMA-based assessment, 79 articles were included in the review. The studies predominantly focused on organizations, including start-ups and Venture Capital funds (46%). Most articles (60%) used qualitative methods, 25% employed mixed methods, and 15% used quantitative methods. Geographically, 63% of articles focused on a single country, primarily the USA (35%), followed by Iran, Sweden, Switzerland, China, and Japan (2–4% each). Coupling analysis identified five topic clusters: crowdfunding for medical research, innovation in medical technology, new product development, digital start-ups, and the venture capital industry. This review highlights the significant role of MedTech start-ups in advancing healthcare innovations despite challenges like regulatory hurdles and high capital requirements. The literature emphasizes the importance of collaboration among universities, industry, and government for successful commercialization. The geographic concentration in the USA indicates a need for more inclusive research. Crowdfunding and venture capital emerge as crucial funding sources, suggesting strategies to mitigate risks and enhance innovation success.

Treatment of human excreta in humanitarian camps remains rare, leading to environmental and public health risks. Fecal sludge treatment (FST) can help reduce these risks. Our objective was to summarize barriers (hindering implementation) and facilitators (enabling implementation) to FST in humanitarian camps to inform guidance. We completed a systematic review of eight databases and 39 websites in 2020, with an update in April 2023. Documents were included if they assessed FST implementation, in a humanitarian camp setting, with primary data collection of at least output level indicators. Overall, 53 documents, including 75 FST interventions from 12 countries were included. We identified 424 barriers and 435 facilitators in 11 categories: performance (239), operation (146), technical (109), economic (78), environmental (59), spatial (55), social/cultural (47), temporal (44), safety (34), supply (29), and institutional (19). The most common facilitators of FST implementation were: high reduction efficiencies; rapid implementation with available technologies; low capital and operational costs; ease of operation and maintenance; and, achieving effluent discharge standards, effluent reuse, and safe discharge. The most common barriers included under- or over-designed systems with inappropriate materials, needing strong operational supervision and additional treatment, with effluents not meeting discharge standards. Future guidance should focus on recommendations to enable facilitators and hinder barriers. Limitations included that most of the research was from one country (Bangladesh), and in stable contexts. Strengths of this work include a holistic, broad, example-based summary of actual FST implementations in camps in humanitarian settings. This review can be used to develop guidance and checklists for implementing FST in humanitarian camps, and future research needed.